STSCI 4030

Last Updated

  • Schedule of Classes - June 11, 2025 2:48PM EDT

STSCI 4030
Linear Models with Matrices

Course information provided by the 2025-2026 Catalog.

The focus of this course is the theory and application of the general linear model expressed in its matrix form. Topics will include: least squares estimation, multiple linear regression, coding for categorical predictors, residual diagnostics, anova decomposition, polynomial regression, model selection techniques, random effects and mixed models, maximum likelihood estimation and distributional theory assuming normal errors. Homework assignments will involve computation using the R statistical package.


Distribution Requirements (DLS-AG, OPHLS-AG), (SDS-AS), (STA-IL)

Last 3 terms offered 2024FA, 2023FA, 2022FA

Learning Outcomes REF-FA25

  • Students will be able to discuss the mathematical foundations of linear statistical models using matrix algebra.
  • Students will be able to use diagnostic measures to assess the validity of a given statistical model.
  • Students will be able to analyze data involving both fixed and random factors.

View Enrollment Information

Syllabi: none

  •   Regular Academic Session.  Choose one lecture and one laboratory. Combined with: BTRY 4030, STSCI 5030

  • 4 Credits Stdnt Opt

  •  4649 STSCI 4030   LEC 001

  •  
    • MW
    • Aug 25 - Dec 8, 2025

    • Kowal, D

  • Instruction Mode: In Person

    For Bowers Computer and Information Science (CIS) Course Enrollment Help, please see: https://tdx.cornell.edu/TDClient/193/Portal/Home/

  •  4741 STSCI 4030   LAB 401

  •  
    • M
    • Aug 25 - Dec 8, 2025

    • Kowal, D

  • Instruction Mode: In Person

  •  4781 STSCI 4030   LAB 402

  •  
    • W
    • Aug 25 - Dec 8, 2025

    • Kowal, D

  • Instruction Mode: In Person

  •  4896 STSCI 4030   LAB 403

  •  
    • W
    • Aug 25 - Dec 8, 2025

    • Kowal, D

  • Instruction Mode: In Person

  •  4897 STSCI 4030   LAB 404

  •  
    • R
    • Aug 25 - Dec 8, 2025

    • Kowal, D

  • Instruction Mode: In Person